IDENTIFICATION OF POTENTIAL BIOMARKERS AND THERAPEUTIC TARGETS FOR ORAL SQUAMOUS CELL CARCINOMA USING BIOINFORMATICS ANALYSIS | ||||
| Alexandria Dental Journal | ||||
| Articles in Press, Corrected Proof, Available Online from 10 February 2025 PDF (410.18 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/adjalexu.2024.340500.1561 | ||||
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| Authors | ||||
Nourhan Abou Madawi  1; Zeinab E. Darwish1; Radwa Mehanna2; Nehal Khalil3; Heba R. Mady1; Enas M. Omar1
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| 1Oral Pathology Department, Faculty of Dentistry, Alexandria University, Alexandria, Egypt | ||||
| 2Medical Physiology Department, Center of Excellence for Research in Regenerative Medicine and Applications (CERRMA), Faculty of Medicine, Alexandria University, Alexandria, Egypt | ||||
| 3Medical Biochemistry Department, Center of Excellence for Research in Regenerative Medicine and Applications (CERRMA), Faculty of Medicine, Alexandria University, Alexandria, Egypt | ||||
| Abstract | ||||
| INTRODUCTION: Oral squamous cell carcinoma is the most common cancer arising in the oral cavity and despite progress in its management, the survival rate has not markedly increased in the last few decades. Bioinformatics has helped in great knowledge about the genetic origins and functional mechanisms influencing human diseases specially cancer which has helped in identifying novel therapeutic drug targets. Thus, bioinformatics can pave the way towards successful management of OSCC. OBJECTIVES: To identify genes driving oncogenesis in OSCC that can help in early diagnosis and developing new effective therapeutic drugs. METHODOLOGY: The bioinformatics online tool UCSC Xena was used to extract RNA-Seq data of OSCC from head and neck cancer dataset in the The Cancer Genome Atlas (TCGA) database. Differential gene expression analysis between tumor and control samples was done by DESeq2 package in R. Functional enrichment analysis of differentially expressed genes (DEGs) was done using DAVID bioinformatics tool. Protein-protein interaction network was represented by STRING. RESULTS: A total of 159 differentially expressed genes (DEGs) between OSCC and normal tissue samples were identified. 151 genes were downregulated and 8 genes were upregulated. GO annotation revealed that they were mainly enriched in keratinization and intermediate filament organization. Moreover, protein-protein interaction network defined intermediate filament organization to be functionally enriched with the genes KRT81, KRT83, KRT76 and KRT36 being highly correlated and significantly downregulated in oral cancer. CONCLUSIONS: The key genes in OSCC identified from functional genomics using bioinformatics analysis can be used as new biomarkers and therapeutic targets for OSCC. | ||||
| Keywords | ||||
| Oral squamous cell carcinoma; Biomarkers; Targeted therapy; Bioinformatics analysis | ||||
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